Higher Frequency Ventilation Attenuates Lung Injury during High-frequency Oscillatory Ventilation in Sheep Models of Acute Respiratory Distress Syndrome
Bibliographic record
Abstract
BACKGROUND: High-frequency oscillatory ventilation (HFOV) at higher frequencies minimizes the tidal volume. However, whether increased frequencies during HFOV can reduce ventilator-induced lung injury remains unknown. METHODS: After the induction of acute respiratory distress syndrome in the model by repeated lavages, 24 adult sheep were randomly divided into four groups (n = 6): three HFOV groups (3, 6, and 9 Hz) and one conventional mechanical ventilation (CMV) group. Standard lung recruitments were performed in all groups until optimal alveolar recruitment was reached. After lung recruitment, the optimal mean airway pressure or positive end-expiratory pressure was determined with decremental pressure titration, 2 cm H2O every 10 min. Animals were ventilated for 4 h. RESULTS: After lung recruitment, sustained improvements in gas exchange and compliance were observed in all groups. Compared with the HFOV-3 Hz and CMV groups, the transpulmonary pressure and tidal volumes were statistically significantly lower in the HFOV-9 Hz group. The lung injury scores and wet/dry weight ratios were significantly reduced in the HFOV-9 Hz group compared with the HFOV-3 Hz and CMV groups. Expression of interleukin-1β and interleukin-6 in the lung tissue, decreased significantly in the HFOV-9 Hz group compared with the HFOV-3 Hz and CMV groups. Malondialdehyde expression and myeloperoxidase activity in lung tissues in the HFOV-9 Hz group decreased significantly, compared with the HFOV-3 Hz and CMV groups. CONCLUSION: The use of HFOV at 9 Hz minimizes lung stress and tidal volumes, resulting in less lung injury and reduced levels of inflammatory mediators compared with the HFOV-3 Hz and CMV conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".